The goal of this study is to examine the impact of certain variables on the climate by examining the AQI of counties across the United States of America using data collected by the EPA.
There are two smaller sub studies in this presentation: One examining the effects of the Climate Alliance legislative program, and another examining the correlation between aspects of counties and the air quality.
To begin we read the data in from the EPA datasets.
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## ## Call: ## lm(formula = med.aqi ~ is.climate.alli + state, data = focus_data) ## ## Residuals: ## Min 1Q Median 3Q Max ## -36.82 -3.06 1.47 5.68 94.52 ## ## Coefficients: (1 not defined because of singularities) ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 37.5294 1.8189 20.63 < 2e-16 *** ## is.climate.alliyes -1.1008 2.3058 -0.48 0.63312 ## stateAlaska -17.0000 3.1503 -5.40 7.6e-08 *** ## stateArizona 6.3167 2.7630 2.29 0.02235 * ## stateArkansas -1.7294 2.7942 -0.62 0.53603 ## stateCalifornia 9.8827 1.7521 5.64 1.9e-08 *** ## stateColorado 0.3419 1.9628 0.17 0.86172 ## stateConnecticut 2.1339 3.0064 0.71 0.47792 ## stateDelaware 4.7381 4.5558 1.04 0.29846 ## stateFlorida -2.3371 2.1795 -1.07 0.28371 ## stateGeorgia -0.8570 2.2908 -0.37 0.70836 ## stateHawaii -8.4286 4.0086 -2.10 0.03562 * ## stateIdaho -12.5294 2.4467 -5.12 3.3e-07 *** ## stateIllinois -1.1323 2.0227 -0.56 0.57570 ## stateIndiana -3.4669 2.1712 -1.60 0.11048 ## stateIowa -1.2169 2.6121 -0.47 0.64136 ## stateKansas -5.2112 2.9019 -1.80 0.07267 . ## stateKentucky 0.5261 2.3219 0.23 0.82076 ## stateLouisiana -2.4068 2.1104 -1.14 0.25423 ## stateMaine -2.0786 2.7627 -0.75 0.45192 ## stateMaryland 1.3950 2.3058 0.60 0.54527 ## stateMassachusetts -0.2986 2.7630 -0.11 0.91394 ## stateMichigan -1.5536 2.0043 -0.78 0.43836 ## stateMinnesota -5.7857 2.1649 -2.67 0.00759 ** ## stateMississippi -0.0056 2.9435 0.00 0.99848 ## stateMissouri -1.0502 2.3773 -0.44 0.65870 ## stateMontana -9.6610 2.5036 -3.86 0.00012 *** ## stateNebraska -11.4739 3.0915 -3.71 0.00021 *** ## stateNevada -0.9286 2.8736 -0.32 0.74662 ## stateNew Hampshire -2.8151 3.3679 -0.84 0.40332 ## stateNew Jersey 2.5089 2.3502 1.07 0.28586 ## stateNew Mexico -4.2411 2.3502 -1.80 0.07130 . ## stateNew York -4.9608 1.9552 -2.54 0.01125 * ## stateNorth Carolina -0.4549 1.8678 -0.24 0.80761 ## stateNorth Dakota -3.2794 2.9887 -1.10 0.27265 ## stateOhio -2.4104 2.1558 -1.12 0.26365 ## stateOklahoma -2.5702 2.3672 -1.09 0.27772 ## stateOregon -8.5350 2.0980 -4.07 4.9e-05 *** ## statePennsylvania 0.9294 1.8432 0.50 0.61412 ## stateRhode Island -0.4286 4.5558 -0.09 0.92506 ## stateSouth Carolina -1.5016 2.5363 -0.59 0.55387 ## stateSouth Dakota -5.8794 2.9887 -1.97 0.04929 * ## stateTennessee -1.9425 2.3986 -0.81 0.41814 ## stateTexas -4.8593 2.1137 -2.30 0.02161 * ## stateUtah 5.7039 2.6566 2.15 0.03191 * ## stateVermont -7.0536 4.0086 -1.76 0.07862 . ## stateVirginia -6.9139 1.9138 -3.61 0.00031 *** ## stateWashington -10.7892 1.9628 -5.50 4.4e-08 *** ## stateWest Virginia -8.3732 2.6121 -3.21 0.00137 ** ## stateWisconsin NA NA NA NA ## stateWyoming 1.6650 2.5363 0.66 0.51158 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 10.6 on 2029 degrees of freedom ## Multiple R-squared: 0.171, Adjusted R-squared: 0.151 ## F-statistic: 8.55 on 49 and 2029 DF, p-value: <2e-16
Climate Alliance states tend to have a better AQI on average but it is not significant.
This might be because the Climate Alliance only went into effect 3 years ago in 2017.
Using the data found by the USDA’s Economic Research Service, we look for predictors in counties to determine air quality and find correlations. This begins by merging the 2019 AQI with the latest USDA ERS data. We use 2019 data to avoid skewing due to the 2020 West Coast fires.
To begin the analysis, we start by merging county data with AQI data. We start by merging all three sets of ERS county data, and then we merge by county and state.
We only take the data from year 2019 to keep it consistent. We are avoiding using 2020 data due to the fires on the West coast skewing data.
Break the cleaned and merged dataset into X and Y for use with cv.glmnet. We use set.seed(1) for consistency.
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## Anova Table (Type II tests) ## ## Response: med.aqi ## Sum Sq Df F value Pr(>F) ## state 15670 48 3.44 2.5e-13 *** ## PctEmpAgriculture 109 1 1.15 0.2848 ## PctEmpConstruction 174 1 1.83 0.1761 ## PctEmpFIRE 734 1 7.73 0.0055 ** ## Age65AndOlderPct2010 50 1 0.53 0.4676 ## Ed4AssocDegreePct 774 1 8.16 0.0044 ** ## FemaleHHPct 1681 1 17.71 2.8e-05 *** ## HH65PlusAlonePct 578 1 6.09 0.0138 * ## Ed3SomeCollegeNum 737 1 7.77 0.0054 ** ## ForeignBornMexNum 610 1 6.43 0.0114 * ## NetMigrationNum0010 1698 1 17.90 2.6e-05 *** ## Residuals 89962 948 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
From the Anova call above, we see that Age65AndOlderPct2010 is the least relevant, so we remove it.
## Anova Table (Type II tests) ## ## Response: med.aqi ## Sum Sq Df F value Pr(>F) ## state 15623 48 3.43 2.9e-13 *** ## PctEmpAgriculture 92 1 0.97 0.3246 ## PctEmpConstruction 143 1 1.50 0.2205 ## PctEmpFIRE 723 1 7.62 0.0059 ** ## Ed4AssocDegreePct 744 1 7.84 0.0052 ** ## FemaleHHPct 1652 1 17.41 3.3e-05 *** ## HH65PlusAlonePct 950 1 10.01 0.0016 ** ## Ed3SomeCollegeNum 732 1 7.72 0.0056 ** ## ForeignBornMexNum 618 1 6.52 0.0108 * ## NetMigrationNum0010 1683 1 17.74 2.8e-05 *** ## Residuals 90012 949 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
From the Anova call above, we see that PctEmpAgriculture is the least relevant, so we remove it.
## Anova Table (Type II tests) ## ## Response: med.aqi ## Sum Sq Df F value Pr(>F) ## state 16002 48 3.51 8.3e-14 *** ## PctEmpConstruction 124 1 1.31 0.25270 ## PctEmpFIRE 1037 1 10.93 0.00098 *** ## Ed4AssocDegreePct 685 1 7.22 0.00732 ** ## FemaleHHPct 1667 1 17.58 3.0e-05 *** ## HH65PlusAlonePct 1046 1 11.03 0.00093 *** ## Ed3SomeCollegeNum 786 1 8.29 0.00408 ** ## ForeignBornMexNum 614 1 6.47 0.01112 * ## NetMigrationNum0010 1704 1 17.96 2.5e-05 *** ## Residuals 90104 950 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
From the Anova call above, we see that PctEmpConstruction is the least relevant, so we remove it.
## Anova Table (Type II tests) ## ## Response: med.aqi ## Sum Sq Df F value Pr(>F) ## state 16606 48 3.65 1.1e-14 *** ## PctEmpFIRE 1127 1 11.88 0.00059 *** ## Ed4AssocDegreePct 733 1 7.73 0.00555 ** ## FemaleHHPct 1974 1 20.81 5.7e-06 *** ## HH65PlusAlonePct 1139 1 12.01 0.00055 *** ## Ed3SomeCollegeNum 814 1 8.58 0.00348 ** ## ForeignBornMexNum 582 1 6.13 0.01347 * ## NetMigrationNum0010 1679 1 17.69 2.8e-05 *** ## Residuals 90228 951 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Estimate Std. Error t value Pr(>|t|) ## PctEmpFIRE 6.13e-01 1.78e-01 3.45 5.91e-04 ## Ed4AssocDegreePct -5.29e-01 1.90e-01 -2.78 5.55e-03 ## FemaleHHPct 5.35e-01 1.17e-01 4.56 5.73e-06 ## HH65PlusAlonePct -4.73e-01 1.36e-01 -3.47 5.53e-04 ## Ed3SomeCollegeNum 1.42e-05 4.86e-06 2.93 3.48e-03 ## ForeignBornMexNum 2.15e-05 8.67e-06 2.48 1.35e-02 ## NetMigrationNum0010 2.78e-05 6.61e-06 4.21 2.84e-05
From the final model, we see that most of the impact on AQI is geographical. For example, the increase from ForeignBornMexNum and NetMigrationNum could signal that states closer to the Mexican border tend to have worse AQIs due to their location. However, the most clear predictors are the states themselves.
The assumptions for linearity appear to hold up until about 1 standard deviation below the mean.
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The overall objective of this study was to use the AQI of counties across the USA to determine the impact of variables on the climate. Using data collected by the EPA, we were able to focus on the effect of the Climate Alliance on curbing the deterioration of the AQI across the nation, as well as the correlation between aspects of counties and their air quality.
From this study, we were able to conclude that the Climate Alliance has not had much of an effect yet on the AQI of member states, but do have better AQIs on average compared to other states. We were also able to see that most of the impact on the AQI is geographical based on the significant variables of the model.